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Role of endoscopic ultrasound and endoscopic ultrasound-guided tissue acquisition in diagnosing hepatic focal lesions

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World Journal of Methodology 2023年第4期13卷 287-295页

作者： Hussein Hassan Okasha Hanane Delsa Abdelmoneim Alsawaf ahmed morad hashim Hani M Khattab Dalia Abdelfatah Abeer Abdellatef Amr Albitar Department of Internal Medicine Division of Gastroenterology and HepatologyKasr Al-Aini School of MedicineCairo UniversityCairo 11451Egypt Department of Gastroenterology and Hepatology Cheikh Khalifa International University HospitalMohammed VI University of Sciences and HealthCasablanca 82403Morocco Research Unit Mohammed VI Center for Research and InnovationRabat 10100Morocco Department of Gastroenterology Barnsley NHS Foundation TrustBarnsley S752EPUnited Kingdom Department of Pathology Kasr Al-Aini School of MedicineCairo UniversityCairo 11451Egypt Department of Cancer Epidemiology and Biostatistics National Cancer InstituteCairo UniversityCairo 11451Egypt

BACKGROUND Endoscopic ultrasonography(EUS)has become an established method in diagnostic and therapeutic procedures in gastroenterology;however,it has recently gained a growing role in *** To evaluate the role of EUS features,strain elastography(SE),and EUS-tissue acquisition in diagnosing hepatic focal lesions(HFLs)that could affect further *** This cross-sectional study included 215 patients with pancreatic,biliary,or gastrointestinal malignancies referred for EUS *** were identified in 43 patients(20%),and EUSguided tissue acquisition was performed from these *** EUS features were highly sensitive(100%)but much less specific(57%)in diagnosing HFLs;the overall accuracy was 94%.Real-time elastography was also very sensitive(97%)but less specific(67%)in diagnosing HFLs;however,the overall accuracy was 92%.EUS tissue acquisition was extremely sensitive(100%)and specific(100%),with a 100%overall diagnostic *** The diagnostic utility of EUS-guided tissue acquisition was extremely accurate in diagnosing *** characteristics and real-time SE accurately predicted the histological diagnosis of both benign and malignant HFLs.

关键词： Endoscopic ultrasonography Hepatic focal lesions Fine needle aspiration Fine needle biopsy Elastography

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A Hybrid and Lightweight Device-to-Server Authentication Technique for the Internet of Things

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Computers, Materials & Continua 2024年第3期78卷 3805-3823页

作者： Shaha Al-Otaibi Rahim Khan hashim Ali Aftab ahmed Khan Amir Saeed Jehad Ali Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Abdul Wali Khan University MardanMardan23200Pakistan Department of Computer Science and IT UET PeshawarJalozai Campus PeshawarPeshawar24240Pakistan Department of AI Convergence Network Ajou UniversitySuwon16499South Korea

The Internet of Things(IoT)is a smart networking infrastructure of physical devices,i.e.,things,that are embedded with sensors,actuators,software,and other technologies,to connect and share data with the respective server *** IoTs are cornerstones in different application domains,the device’s authenticity,i.e.,of server(s)and ordinary devices,is the most crucial issue and must be resolved on a priority ***,various field-proven methodologies were presented to streamline the verification process of the communicating devices;however,location-aware authentication has not been reported as per our knowledge,which is a crucial metric,especially in scenarios where devices are *** paper presents a lightweight and location-aware device-to-server authentication technique where the device’s membership with the nearest server is subjected to its location information along with other ***,Media Access Control(MAC)address and Advance Encryption Scheme(AES)along with a secret shared key,i.e.,λ_(i) of 128 bits,have been utilized by Trusted Authority(TA)to generate MaskIDs,which are used instead of the original ID,for every device,i.e.,server and member,and are shared in the offline ***,TA shares a list of authentic devices,i.e.,server S_(j) and members C_(i),with every device in the IoT for the onward verification process,which is required to be executed before the initialization of the actual communication ***,every device should be located such that it lies within the coverage area of a server,and this location information is used in the authentication process.A thorough analytical analysis was carried out to check the susceptibility of the proposed and existing authentication approaches against well-known intruder attacks,i.e.,man-in-the-middle,masquerading,device,and server impersonations,etc.,especially in the IoT ***,proposed authentication and existing state-of-the-art approaches have been s

关键词： Internet of things authenticity security location communication

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Exploring Trees Diversity and Ecological Value of Mountain Forests in the Blue Nile Region of Sudan

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Open Journal of Ecology 2024年第3期14卷 245-256页

作者： Dafa-Alla M. Dafa-Alla Haytham H. Gibreel ahmed A. H. Siddig Yousif E. Yagoub hashim A. Abdelkarim Khatir H.T. Saeed ahmed I. Abuelbashar Faculty of Forestry-Shambat University of Khartoum Khartoum Sudan Harvard Forest Harvard University Petersham MA USA Faculty of Forest Science and Wood Technology University of Gezira Wad-Medani Sudan Department of Forestry and Range Sciences Faculty of Natural Resources and Environmental Studies Alsalam University Alfula Sudan Forest Research and Gum Arabic Centre Agricultural Research Corporation Khartoum-Soba Sudan

We carried out this research at Abu-Gaddaf Natural Forest Reserve (ANFR) which is located east of Blue Nile River, in Blue Nile State, Sudan. It aims at exploring tree composition assessing their diversity indices, and ecological importance values. For survey of types and estimation of density of tree species in the forest, we randomly distributed 97 circular sample plots (0.1 hectare (ha)). In each sample plot we identified all trees to the species level, recorded their frequencies and computed species diversity and importance value indices (IVI). A total of 13 tree species, which belong to 12 genera and nine families, were identified in the tree layer of the forest. Mean tree density in ANFR was 116 trees/ha, composed mainly of Boswellia papyrifera (Del.) Hochst. (48), followed by Combretum hartmannianu (19) and Lannea fruticosa (18). Fabaceae was the most common family followed by Combretaceae and Malvaceae. B. papyrifera Delile Hochst was the most abundant while Acacia seyal was the least abundant species. Species richness (R = 1.71), evenness (E = 0.69), dominance (0.24) indices and Simpson’s Index of Diversity (D' = 0.76) suggest a moderate diversity, moderate numbers of individuals in each species and a moderate community stability. The research provides empirical results on diversity and ecological importance value of species, signifies the urging need to safeguard threatened species and to prioritize them for conservation, as well as the need to promote management of abundant species to provide multiple forest ecosystem services.

关键词： Tree Species Diversity Dry-Land Forest Sudan

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Threefold Optimized Forecasting of Electricity Consumption in Higher Education Institutions

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Computers, Materials & Continua 2022年第11期73卷 2351-2370页

作者： Majida Kazmi hashim Raza Khan Lubaba Mohammad Hashir Bin Khalid Saad ahmed Qazi Faculty of Electrical and Computer Engineering NED University of Engineering and TechnologyKarachi75270Pakistan Neurocomputation Lab National Center of Artificial IntelligenceKarachi75270Pakistan

Energy management benefits both consumers and utility companiesalike. Utility companies remain interested in identifying and reducing energywaste and theft, whereas consumers’ interest remain in lowering their energyexpenses. A large supply-demand gap of over 6 GW exists in Pakistan asreported in 2018. Reducing this gap from the supply side is an expensiveand complex task. However, efficient energy management and distributionon demand side has potential to reduce this gap economically. Electricityload forecasting models are increasingly used by energy managers in takingreal-time tactical decisions to ensure efficient use of resources. Advancementin Machine-learning (ML) technology has enabled accurate forecasting ofelectricity consumption. However, the impact of computation cost affordedby these ML models is often ignored in favour of accuracy. This studyconsiders both accuracy and computation cost as concurrently significantfactors because together they shape the technology environment as well ascreate economic impact. Thus, a three-fold optimized load forecasting modelis proposed which includes (1) application specific parameters selection, (2)impact of different dataset granularities and (3) implementation of specificdata preparation. It deploys and compares the widely used back-propagationArtificial Neural Network (ANN) and Random Forest (RF) models for theprediction of electricity consumption of buildings within a university. In addition to the temporal and historical power consumption date as input parameters, the study also embeds weather data as well as university operationalcalendars resulting in improved performance. The outcomes are indicativethat the granularity i.e. the scale of details in data, and set of reduced and fullinput parameters impact performance accuracies differently for ANN and RFmodels. Experimental results show that overall RF model performed betterboth in terms of accuracy as well as computational time for a 1-min, 15-minand 1-h dataset

关键词： Electricity forecasting short term higher educational institution artificial neural network random forest accuracy computational time

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Chaotic Flower Pollination with Deep Learning Based COVID-19 Classification Model

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Computers, Materials & Continua 2023年第3期74卷 6195-6212页

作者： T.Gopalakrishnan Mohamed Yacin Sikkandar Raed Abdullah Alharbi P.Selvaraj Zahraa H.Kareem ahmed Alkhayyat Ali hashim Abbas Department of Information Technology Manipal Institute of Technology BengaluruManipal Academy of Higher EducationManipalIndia Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Public Health Department College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Computing Technologies College of Engineering and TechnologyFaculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University CollegeBabylonIraq College of Technical Engineering The Islamic UniversityNajafIraq College of Information Technology Imam Ja’afar Al-Sadiq UniversityAl-Muthanna66002Iraq

The Coronavirus Disease(COVID-19)pandemic has exposed the vulnerabilities of medical services across the globe,especially in underdeveloped *** the aftermath of the COVID-19 outbreak,a strong demand exists for developing novel computer-assisted diagnostic tools to execute rapid and cost-effective screenings in locations where many screenings cannot be executed using conventional *** imaging has become a crucial component in the disease diagnosis process,whereas X-rays and Computed Tomography(CT)scan imaging are employed in a deep network to diagnose the *** general,four steps are followed in image-based diagnostics and disease classification processes by making use of the neural networks,such as network training,feature extraction,model performance testing and optimal feature *** current research article devises a Chaotic Flower Pollination Algorithm with a Deep Learning-Driven Fusion(CFPADLDF)approach for detecting and classifying *** presented CFPA-DLDF model is developed by integrating two DL models to recognize COVID-19 in medical ***,the proposed CFPA-DLDF technique employs the Gabor Filtering(GF)approach to pre-process the input *** addition,a weighted voting-based ensemble model is employed for feature extraction,in which both VGG-19 and the MixNet models are ***,the CFPA with Recurrent Neural Network(RNN)model is utilized for classification,showing the work’s novelty.A comparative analysis was conducted to demonstrate the enhanced performance of the proposed CFPADLDF model,and the results established the supremacy of the proposed CFPA-DLDF model over recent approaches.

关键词： Deep learning medical imaging fusion model chaotic models ensemble model COVID-19 detection

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Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

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Computer Systems Science & Engineering 2023年第5期45卷 1711-1726页

作者： Manar ahmed Hamza Aisha Hassan Abdalla hashim Dalia H.Elkamchouchi Nadhem Nemri Jaber S.Alzahrani Amira Sayed A.Aziz Mnahel ahmed Ibrahim Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt College of Science and Humanities Studies in Alquwayiyah Shaqra UniversitySaudi Arabia

Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the *** the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the *** node clustering is a popular approach for ***,the sensor nodes are grouped to form clusters in a cluster-based WSN *** battery performance of the sensor nodes is likewise *** a result,the energy efficiency of WSNs is *** specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in *** the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was *** selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed *** provides the best routing path and increases the lifetime and energy efficiency of the ***-to-end delay and packet loss rate have also been *** proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor *** to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.

关键词： Wireless sensor networks energy-efficient load balancing energy consumption network’s lifetime cluster heads grey wolf optimization tabu search particle swarm optimization

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Hyperparameter Tuned Deep Learning Enabled Intrusion Detection on Internet of Everything Environment

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Computers, Materials & Continua 2022年第12期73卷 6579-6594页

作者： Manar ahmed Hamza Aisha Hassan Abdalla hashim Heba G.Mohamed Saud S.Alotaibi Hany Mahgoub Amal S.Mehanna Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computers and Information Computer Science DepartmentMenoufia UniversityEgypt Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt

Internet of Everything(IoE),the recent technological advancement,represents an interconnected network of people,processes,data,and *** recent times,IoE gained significant attention among entrepreneurs,individuals,and communities owing to its realization of intense values from the connected *** the other hand,the massive increase in data generation from IoE applications enables the transmission of big data,from contextawaremachines,into useful *** and privacy pose serious challenges in designing IoE environment which can be addressed by developing effective Intrusion Detection Systems(IDS).In this background,the current study develops Intelligent Multiverse Optimization with Deep Learning Enabled Intrusion Detection System(IMVO-DLIDS)for IoT *** presented IMVO-DLIDS model focuses on identification and classification of intrusions in IoT *** proposed IMVO-DLIDS model follows a three-stage *** first,data pre-processing is performed to convert the actual data into useful *** addition,Chaotic Local Search Whale Optimization Algorithm-based Feature Selection(CLSWOA-FS)technique is employed to choose the optimal feature ***,MVO algorithm is exploited with Bidirectional Gated Recurrent Unit(BiGRU)model for ***,the novelty of the work is the application of MVO algorithm in fine-turning the hyperparameters involved in BiGRU *** experimental validation was conducted for the proposed IMVO-DLIDS model on benchmark datasets and the results were assessed under distinct *** extensive comparative study was conducted and the results confirmed the promising outcomes of IMVO-DLIDS approach compared to other approaches.

关键词： Internet of everything deep learning feature selection classification intrusion detection cybersecurity

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Predictors of thirty-day readmission in nonagenarians presenting with acute heart failure with preserved ejection fraction: a nationwide analysis

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Journal of Geriatric Cardiology 2021年第12期18卷 1008-1018页

作者： ahmed Maraey Mahmoud Salem Nabila Dawoud Mahmoud Khalil ahmed Elzanaty Hadeer Elsharnoby ahmed Younes ahmed hashim Amit Alam Department of Internal Medicine the CHI St.Alexius HealthBismarckUSA Department of Internal Medicine University of North DakotaBismarckUSA Center for Advanced Heart and Lung Diseases Baylor University Medical CenterDallasUSA Department of Internal Medicine University of KentuckyLexingtonUSA Department of Internal Medicine Lincoln Medical Center Weil Cornell UniversityBronxUSA Department of Cardiovascular Medicine Tanta UniversityTantaEgypt Department of Cardiovascular Disease University of ToledoToledoUSA Faculty of Medicine Tanta UniversityTantaEgypt Department of Internal Medicine East Carolina UniversityGreenvilleUSA Faculty of Medicine Ain Shams UniversityCairoEgypt College of Medicine Texas A&M Health Science CenterDallasUSA

BACKGROUD Acute heart failure with preserved ejection fraction(HFpEF)is a common but poorly studied cause of hospital admissions among *** study aimed to evaluate predictors of thirty-day readmission,in-hospital mortality,length of stay,and hospital charges in nonagenarians hospitalized with acute *** Patients hospitalized between January 2016 and December 2018 with a primary diagnosis of diastolic heart failure were identified using ICD-10 within the Nationwide Readmission *** excluded patients who died in index admission,and discharged in December each year to allow thirty-day *** regression was performed on each ***-ables with P-value<0.2 were included in the multivariate regression *** From a total of 45,393 index admissions,43,646 patients(96.2%)survived to discharge.A total of 7,437 patients(15.6%)had a thirty-day *** cost of readmission was 43,265 United States dollars(USD)per *** predictors of thirty-day readmission were chronic kidney disease stage III or higher[adjusted odds ratio(aOR)=1.20,95%CI:1.07−1.34,P=0.002]and diabetes mellitus(aOR=1.18,95%CI:1.07−1.29,P=0.001).Meanwhile,female(aOR=0.90,95%CI:0.82−0.99,P=0.028)and palliative care encounter(aOR=0.27,95%CI:0.21−0.34,P<0.001)were associated with lower odds of *** arrhythmia(aOR=1.46,95%CI:1.11−1.93,P=0.007)and aortic stenosis(aOR=1.36,95%CI:1.05−1.76,P=0.020)were amongst predictors of in-hospital *** In nonagenarians hospitalized with acute HFpEF,thirty-day readmission is common and *** co-morbidities predict poor *** strategies need to be developed to improve the quality of care and prevent the poor outcome in nonagenarians.

关键词： fraction nationwide dollar

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Spotted Hyena Optimizer with Deep Learning Driven Cybersecurity for Social Networks

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Computer Systems Science & Engineering 2023年第5期45卷 2033-2047页

作者： Anwer Mustafa Hilal Aisha Hassan Abdalla hashim Heba G.Mohamed Lubna A.Alharbi Mohamed K.Nour Abdullah Mohamed ahmed S.Almasoud Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversityTabuk47512Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMecca24382Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityRiyadh12435Saudi Arabia

Recent developments on Internet and social networking have led to the growth of aggressive language and hate *** provocation,abuses,and attacks are widely termed cyberbullying(CB).The massive quantity of user generated content makes it difficult to recognize *** advancements in machine learning(ML),deep learning(DL),and natural language processing(NLP)tools enable to detect and classify CB in social *** this view,this study introduces a spotted hyena optimizer with deep learning driven cybersecurity(SHODLCS)model for *** presented SHODLCS model intends to accomplish cybersecurity from the identification of CB in the *** achieving this,the SHODLCS model involves data pre-processing and TF-IDF based feature *** addition,the cascaded recurrent neural network(CRNN)model is applied for the identification and classification of ***,the SHO algorithm is exploited to optimally tune the hyperparameters involved in the CRNN model and thereby results in enhanced classifier *** experimental validation of the SHODLCS model on the benchmark dataset portrayed the better outcomes of the SHODLCS model over the recent approaches.

关键词： Cybersecurity cyberbullying online social network deep learning spotted hyena optimizer

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Hybrid Deep Learning Enabled Load Prediction for Energy Storage Systems

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Computers, Materials & Continua 2023年第5期75卷 3359-3374页

作者： Firas Abedi Hayder M.A.Ghanimi Mohammed A.M.Sadeeq ahmed Alkhayyat Zahraa H.Kareem Sarmad Nozad Mahmood Ali hashim Abbas Ali S.Abosinnee Waleed Khaild Al-Azzawi Mustafa Musa Jaber Mohammed Dauwed Department of Mathematics College of EducationAl-Zahraa University for WomenKarbalaIraq Biomedical Engineering Department College of EngineeringUniversity of Warith Al-AnbiyaaKarbalaIraq ITM Department Technical College of AdministrationDuhok Polytechnic UniversityDuhokIraq College of Technical Engineering The Islamic UniversityNajafIraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityKirkuk36013Iraq College of Information Technology Imam Ja’afar Al-Sadiq UniversityAl-Muthanna66002Iraq Altoosi University College NajafIraq Department of Medical Instruments Engineering Techniques Al-Farahidi UniversityBagdadIraq Department of Medical Instruments Engineering Techniques Al-Turath University CollegeBaghdad10021Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi UniversityBaghdad10021Iraq Department of Medical Instrumentations Techniques Engineering Dijlah University CollegeBaghdadIraq

Recent economic growth and development have considerably raised energy consumption over the *** load prediction approaches become essential for effective planning,decision-making,and contract evaluation of the power *** order to achieve effective forecasting outcomes with minimumcomputation time,this study develops an improved whale optimization with deep learning enabled load prediction(IWO-DLELP)scheme for energy storage systems(ESS)in smart grid *** major intention of the IWO-DLELP technique is to effectually forecast the electric load in SG environment for designing proficient *** proposed IWO-DLELP model initially undergoes pre-processing in two stages namely min-max normalization and feature ***,partition clustering approach is applied for the decomposition of data into distinct clusters with respect to distance and objective ***,IWO with bidirectional gated recurrent unit(BiGRU)model is applied for the prediction of load and the hyperparameters are tuned by the use of IWO *** experiment analysis reported the enhanced results of the IWO-DLELP model over the recent methods interms of distinct evaluation measures.

关键词： Load forecasting smart grid energy storage system electricity load forecasting artificial intelligence clustering

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